This application relates to arc welding and, more specifically, to a method and apparatus for using a neural network to aid in controlling an arc welding process.
Conventional automatic arc welding devices are often used to perform a type of weld called a "blind side weld." In blind side welds, such as welds on jet exhaust assemblies, closure welds on the Space Shuttle main engine, tube and pipeline welds, etc., the back side of the weld cannot be monitored by a sensor or a human operator and partial penetration cannot be detected by sight.
In conventional arc welding systems a degree of penetration, i.e., a degree of depth of the weld with respect to the thickness of the material being welded affects the solidity of the weld. Solid welds are achieved only with full penetration. A welding operation that contains more than a given number of partial penetration welds may not be satisfactory and may need to be replaced. Thus, it is desirable to be able to detect and correct partial penetration during the welding process.
Any arc welding process produces a molten pool of welding material. In conventional arc welding devices, a vibration rate of a molten weld pool is different in a fully penetrated weld than in an incompletely penetrated weld. This vibration difference can be detected, and output as a voltage signal. However, due to a low signal to noise ratio and mixed signal information in the voltage data, conventional signal processing techniques are unable to distinguish useful and reliable penetration information from background noise and other types of extraneous signals. It is, therefore, desirable to be able to reliably distinguish penetration information from such a voltage signal.